用极简数据和低资源实现高精度语音识别，适合边缘设备部署。
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural Network
- 采用轻量1D-CNN模型，适配小规模数据集
- 验证准确率达97.87%，有效应对噪声与样本不足
- 开源代码、数据集与模型，便于复现与落地
语音识别与说话人识别在安全与个人助理等应用中至关重要。本文提出一种轻量级一维卷积神经网络（1D-CNN），旨在利用极小数据集完成说话人识别任务。该方法通过数据增强技术缓解背景噪声与训练样本有限的问题，在验证集上达到97.87%的准确率。未来工作将拓展至更大数据集，并引入迁移学习以提升泛化能力。所有代码、自建数据集及训练模型均已开源，可访问GitHub仓库：https://github.com/IrfanNafiz/RecMe，支持研究复现与实际应用。
原文摘要 · Abstract (English)
Voice recognition and speaker identification are vital for applications in security and personal assistants. This paper presents a lightweight 1D-Convolutional Neural Network (1D-CNN) designed to perform speaker identification on minimal datasets. Our approach achieves a validation accuracy of 97.87%, leveraging data augmentation techniques to handle background noise and limited training samples. Future improvements include testing on larger datasets and integrating transfer learning methods to enhance generalizability. We provide all code, the custom dataset, and the trained models to facilitate reproducibility. These resources are available on our GitHub repository: https://github.com/IrfanNafiz/RecMe.
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